ArticleJournal of personalized medicine2022
Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- QTcNet: a deep learning model for direct heart rate corrected QT interval estimation.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- A MATLAB Algorithm to Automatically Estimate the QT Interval and Other ECG Parameters and Validation Using a Machine Learning Approach in Congenital Long-QT Syndrome.Journal of cardiovascular translational research · 2025Article
- Multimodal fusion learning for long QT syndrome pathogenic genotypes in a racially diverse population.NPJ digital medicine · 2024Article
- From genes to clinical management: A comprehensive review of long QT syndrome pathogenesis and treatment.Heart rhythm O2 · 2024Review
- Artificial Intelligence in Diagnosis of Long QT Syndrome: A Review of Current State, Challenges, and Future Perspectives.Mayo Clinic proceedings. Digital health · 2024Review
- More than 30 years of Brugada syndrome: a critical appraisal of achievements and open issues.Herzschrittmachertherapie & Elektrophysiologie · 2024Review
- The diagnostic value of electrocardiogram-based machine learning in long QT syndrome: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2023Review
Corrections and comments
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Authors and funding
18 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe long-QT syndrome (LQTS) is the most common ion channelopathy, typically presenting with a prolonged QT interval and clinical symptoms such as syncope or sudden cardiac death. Patients may present with a concealed phenotype making the diagnosis challenging. Correctly diagnosing at-risk patients is pivotal to starting early preventive treatment.
objectiveIdentification of congenital and often concealed LQTS by utilizing novel deep learning network architectures, which are specifically designed for multichannel time series and therefore particularly suitable for ECG data. DESIGN AND
resultsA retrospective artificial intelligence (AI)-based analysis was performed using a 12-lead ECG of genetically confirmed LQTS (
conclusionsIn this study, the XceptionTime model outperformed the FCN model for LQTS patients with even better results than in prior studies. Even when a patient cohort with cardiovascular comorbidities is used. AI-based ECG analysis is a promising step for correct LQTS patient identification, especially if common diagnostic measures might be misleading.
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Registered trials
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